Academic literature on the topic 'Swarm based design'
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Journal articles on the topic "Swarm based design"
von Mammen, Sebastian, Scott Novakowski, Gerald Hushlak, and Christian Jacob. "Evolutionary Swarm Design: How Can Swarm-based Systems Help to Generate and Evaluate Designs?" Design Principles and Practices: An International Journal—Annual Review 3, no. 3 (2009): 371–86. http://dx.doi.org/10.18848/1833-1874/cgp/v03i03/37691.
Full textMukhlish, Faqihza, John Page, and Michael Bain. "Evolutionary-learning framework: improving automatic swarm robotics design." International Journal of Intelligent Unmanned Systems 6, no. 4 (October 8, 2018): 197–215. http://dx.doi.org/10.1108/ijius-06-2018-0016.
Full textYao, Wenting, and Yongjun Ding. "Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm." Complexity 2020 (December 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/6693411.
Full textYEN, GARY G., and MOAYED DANESHYARI. "DIVERSITY-BASED INFORMATION EXCHANGE AMONG MULTIPLE SWARMS IN PARTICLE SWARM OPTIMIZATION." International Journal of Computational Intelligence and Applications 07, no. 01 (March 2008): 57–75. http://dx.doi.org/10.1142/s1469026808002144.
Full textBozhinoski, Darko, and Mauro Birattari. "Towards an integrated automatic design process for robot swarms." Open Research Europe 1 (September 27, 2021): 112. http://dx.doi.org/10.12688/openreseurope.14025.1.
Full textLiu, Hanmin, Qinghua Wu, and Xuesong Yan. "Relay Optimization Design Algorithm Based on Swarm Intelligence." Research Journal of Applied Sciences, Engineering and Technology 6, no. 1 (June 5, 2013): 165–70. http://dx.doi.org/10.19026/rjaset.6.4053.
Full textQuanxi Feng, Liu Sanyang, Zhang Jianke, and Yang Guoping. "Extrapolated particle swarm optimization based on orthogonal design." Journal of Convergence Information Technology 7, no. 2 (February 29, 2012): 141–52. http://dx.doi.org/10.4156/jcit.vol7.issue2.17.
Full textYan, Xue Song, Qing Hua Wu, Cheng Yu Hu, and Qing Zhong Liang. "Circuit Design Based on Particle Swarm Optimization Algorithms." Key Engineering Materials 474-476 (April 2011): 1093–98. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1093.
Full textSarangi, Archana, Shubhendu Kumar Sarangi, Sasmita Kumari Padhy, Siba Prasada Panigrahi, and Bijay Ketan Panigrahi. "Swarm intelligence based techniques for digital filter design." Applied Soft Computing 25 (December 2014): 530–34. http://dx.doi.org/10.1016/j.asoc.2013.06.001.
Full textZhu, Xiaoshu, Jie Zhang, and Junhong Feng. "Multiobjective Particle Swarm Optimization Based on PAM and Uniform Design." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/126404.
Full textDissertations / Theses on the topic "Swarm based design"
Kayser, Markus (Markus A. ). "Towards swarm-based design : distributed and materially-tunable digital fabrication across scales." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115741.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 141-149).
Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning, on December 8, 2017 in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Media, Arts and Sciences at the Massachusetts Institute of Technology Throughout history, Nature has always been part of the discourse in Design theory and practice. The Digital Age in Design brings about new computational tools, redefining the role of Nature in Design. In this thesis, I aim to expand the role of Nature in Design and digital fabrication by investigating distributed fabrication strategies for the production of constructs that are, at once, large in scale and materially tunable towards swarm-based design. Digital fabrication approaches can be classified with respect to two basic attributes: (1) the degree of material tailorability, and (2) the level of collaboration between fabrication units. Conventional manufacturing is typically confined to only one of these attribute axes, with certain approaches utilizing complex tunable materials but virtually no collaboration, and others assembling pre-fabricated building blocks with high levels of intercommunication between fabrication units. A similar pattern is mirrored in biological systems: silkworms, for example, deposit a multifunctional tunable material with minimal communication between organisms; while ants, bees and termites operate as multi-agent communicative entities assembling larger constructs out of simple, unifunctional, 'generic' materials. The purpose of this thesis is to depart from these uniaxial manufacturing approaches and develop a novel swarm-inspired distributed digital fabrication method capable of producing tunable multifunctional materials that is also collaborative. This research merges fiber-based digital fabrication and swarm-based logic to produce a system capable of digitally fabricating complex objects and large-scale architectural components through a novel multi-robotic fabrication paradigm. I hypothesize that this design approach-its theoretical foundations, methodological set up and related tools and technologies-will ultimately enable the design of large-scale structures with high spatial resolution in manufacturing that, like biological swarms, can tune their material make-up relative to their environment during the process of construction. Building on the insights derived from case study projects, fabricating with silkworms, ants, and bees, I demonstrate the design and deployment of a multi-robotic system erecting a 4.5-meter tall structure from fiber composites This thesis addresses the current limitations of digital fabrication, namely: (a) the material limitation, through automated digital fabrication of structural multi-functional materials; (b) the gantry limitation, through the construction of large components from a swarm of cooperative small scale robots; and (c) the method limitation, through digital construction methods that are not limited to layered manufacturing, but also support free-form printing (i.e. 3D-printing without support materials), CNC woven constructions and digitally aggregated constructions.
by Markus Kayser.
Ph. D.
Hymes, Connor. "Above the Street: Connecting Buildings and People Through Agent-Based Design Interactions." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1491304988826573.
Full textTai, Hio Kuan. "Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948431.
Full textTakai, Tomohiro. "Simulation based design for high speed sea lift with waterjets by high fidelity urans approach." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/748.
Full textChiusoli, Alberto. "Hi-wire membranes. Progetto di ambienti termali a Bagni San Filippo (Si). Tettonica basata sull'auto-organizzazione di micro-membrature integrate a sistemi di membrane." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Find full textLin, Chun-Yi, and 林駿逸. "Swarm Intelligence based Structural Optimization Design." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/98242027221785521497.
Full text大同大學
機械工程學系(所)
99
In this dissertation, two novel approaches to swarm intelligence-based methodology for optimal design of continuum structural topology and truss structure are presented. One is the ant colony algorithm mimicking the behavior of real ant colonies, and the other is the particle swarm optimization algorithm mimicking the social behavior of bird flocking. In terms of optimal design of structure topology, ant colony algorithm and binary particle swarm optimization algorithm were implemented for finding optimal solutions to multi-model structural problems. Four well-studies benchmark examples in continuum structural topology optimization problems were used to evaluate the proposed approach. The results indicate the effectiveness of the proposed algorithm. And, in terms of optimal design of truss structure, truss structure optimization considering topology, sizing, and shaping simultaneously. A two-stage ant algorithm, consisting of the ant colony algorithm and API(after "apicails" in Pachycondyla apicails) algorithm and a two-stage particle swarm optimization algorithm, consisting of the binary particle swarm optimization and the attractive and repulsive particle swarm optimization were proposed in this thesis for finding optimal truss structure. First, ant colony algorithm and binary particle swarm optimization were used to optimize the topology of truss, and then API algorithm and attractive and repulsive particle swarm optimization ware used to optimize the size and shape of truss. To confirm the effectiveness of the proposed method, several well-know truss optimization problem were used to evaluate the proposed approach. The results indicated that the proposed algorithm have better performance than those reported in the literature.
Huang, Zhi-Liang, and 黃智樑. "LQ Regulator Design Based on Particle Swarm Optimization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/95075383637967356282.
Full text國立高雄海洋科技大學
輪機工程研究所
94
In this paper, a particle swarm optimization (PSO) based linear-quadratic (LQ) state-feedback regulator is investigated. The parameters of LQ regulators are determined by PSO method. A practical example of a rotating inverse pendulum is provided to demonstrate the effectiveness of the PSO-based LQ regulators. The performance of rotating inverse pendulum controlled by PSO-based LQ regulators is more ideal than the performance of rotating inverse pendulum controlled by Traditional LQ regulators. The goal of this study, stabilized the system performance with unstable operation point, can be achieved by using the proposed controller.
Chung, Chen Po, and 陳柏仲. "The Team Character Design Based on Particle Swarm Optimization." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/52766818891642558926.
Full text東海大學
資訊工程與科學系
94
Computer games have highly interactive ability and can integrate various media. Playing computer games have become people’s popular entertainment. Computer games have truly image and sound effect can give the player a rich game experence. But the technology of computer graphics alreay reach a bottleneck in the recent years. So many computer games developers have paid their attention to the AI of game characteristic. They hope the smart and various AI can make the computer game more interesting. The most computer game today use the rule-base design approach because of the simple and easy to implement. But, if the player find the weak point of the computer character, nothing can stop the player to win the game. If the computer character can learn from mistake, there may be a solution of this problem. Some scholar try to implement some learning algorithm to the computer game. But we found it needs large computation and collecting the train data sometimes are difficult. And we found the team work is easily to be found in today’s computer games. So we try to give a new approach to the team play computer game’s AI. For the application of computer game AI, the computation must be quick and stable. Particle Swarm Optimization(PSO) is a new optimization and machine learning technology in Artificial Intelligence. PSO is easy to emplement and there are few parameters to adjust. So we try to implement the PSO as the learning algorithm of computer game characteristic. But according to PSO, there is no coordination between each particle. So it can only create a powerful single character. So we propose a new learning strategy placing the emphasis on the team learning. In summery, this paper proposes a novel method based on PSO to help behavior design in computer games. Compare with the traditional PSO, proposed method can create more efficient team. And there is no need of large computation and training date, which suit the application of computer game. This new mechanism can help AI developer adjust the behavioral parameters which can save the testing time of different combination of parameter. In the experimental results, the proposed mechanism was embedded to design the team bots that indeed presents more changeable and the stable learning characteristic in the Quake III team play mode : Catch the flag.
Huang, Ching-Ya, and 黃靜雅. "Design of Digital Filters Based on Particle Swarm Optimizations." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/35569848762909019671.
Full text國立高雄應用科技大學
電子工程系
97
This paper aims to design a digital filter via Particle Swarm Optimization (PSO). Emulating the collective behavior of creature, the algorithm avoids the local optimal problem and has high convergence speed to optimize the stopband attenuation of the digital filter from the searching domain. Both low pass filter and high pass filter are designed with PSO and Frequency Sampling Method (FSM). Simulation results show that the performance of the proposed method is better than that of Genetic algorithm (GA).
Chang, Dai-Ming, and 張戴明. "Design of FIR digital filter based on particle swarm optimizations." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/68404081644509164315.
Full text樹德科技大學
電腦與通訊研究所
95
Digital filter design is an integral part of the DSP field. Two types of filter structures are the finite impulse response (FIR) filter and the infinite impulse response (IIR) filter, respectively. For a given filtering characteristic, FIR filter may require many system terms to achieve the desired characteristic, whereas IIR filter generally needs fewer terms to achieve the same goal. Furthermore, the FIR filter is inherently guaranteed to be stable, but the stability for the IIR filter depends highly on the choices of filter parameters. The main contribution of this thesis is to apply the optimal search algorithm, particle swarm optimization (PSO), to the design of digital FIR filter. Three different kinds of filter designs are considered in the thesis. First, we apply the PSO algorithm to estimate the optimal coefficients of digital FIR filter. In this case, the order of FIR filter is assumed to be previously known. Second, a higher-order digital differentiator design is proposed via the same PSO algorithm. Four cases of linear phase FIR filters can be designed to match the prescribed differentiation frequency response of digital differentiator. We finally extend the filter design method from one dimension to two dimensions. According to the symmetry and/or anti-symmetry of its two-dimensional impulse responses in both directions and filter lengths, it can be divided into sixteen filter types. Each of them can be taken to design certain desired frequency response in two-dimensional cases by the proposed PSO algorithm.
Books on the topic "Swarm based design"
Lepora, Nathan F. Decision making. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0028.
Full textBook chapters on the topic "Swarm based design"
Wan, Alfred D. M. "Experimental Swarm Design." In Innovative Concepts for Agent-Based Systems, 92–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45173-0_7.
Full textMartínez Soltero, Erasmo Gabriel, Carlos Lopéz-Franco, Alma Y. Alanis, and Nancy Arana-Daniel. "Outdoor Robot Navigation Based on Particle Swarm Optimization." In Fuzzy Logic in Intelligent System Design, 225–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67137-6_24.
Full textSingh, Raj Mohan. "Genetic Algorithm Based Optimal Design of Hydraulic Structures with Uncertainty Characterization." In Swarm, Evolutionary, and Memetic Computing, 742–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_87.
Full textBose, Digbalay, Souvik Kundu, Subhodip Biswas, and Swagatam Das. "Circular Antenna Array Design Using Novel Perturbation Based Artificial Bee Colony Algorithm." In Swarm, Evolutionary, and Memetic Computing, 459–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_54.
Full textWilljuice Iruthayarajan, M., and S. Baskar. "Covariance Matrix Adapted Evolution Strategy Based Design of Mixed H2/H ∞ PID Controller." In Swarm, Evolutionary, and Memetic Computing, 171–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17563-3_21.
Full textVitayasak, Srisatja, and Pupong Pongcharoen. "Genetic Algorithm Based Robust Layout Design By Considering Various Demand Variations." In Advances in Swarm and Computational Intelligence, 257–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20466-6_28.
Full textWang, Frank Xuyan. "Design Index-Based Hedging: Bundled Loss Property and Hybrid Genetic Algorithm." In Advances in Swarm and Computational Intelligence, 266–75. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20466-6_29.
Full textKorb, Oliver, Thomas Stützle, and Thomas E. Exner. "PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design." In Ant Colony Optimization and Swarm Intelligence, 247–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839088_22.
Full textCarrese, Robert, and Xiaodong Li. "Preference-Based Multiobjective Particle Swarm Optimization for Airfoil Design." In Springer Handbook of Computational Intelligence, 1311–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_67.
Full textMoslah, Mariem, Mohamed Aymen Ben HajKacem, and Nadia Essoussi. "Spark-Based Design of Clustering Using Particle Swarm Optimization." In Clustering Methods for Big Data Analytics, 91–113. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97864-2_5.
Full textConference papers on the topic "Swarm based design"
Bharat, Tadikonda Venkata. "Agents based algorithms for design parameter estimation in contaminant transport inverse problems." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668312.
Full textYan, Chuan, Ganesh K. Venayagamoorthy, and Keith A. Corzine. "Implementation of a PSO based online design of an optimal excitation controller." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668330.
Full textWang, Lingfeng, and Chanan Singh. "PSO-Based Multi-Criteria Optimum Design of A Grid-Connected Hybrid Power System With Multiple Renewable Sources of Energy." In 2007 IEEE Swarm Intelligence Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sis.2007.367945.
Full textvan den Berg, B., R. van Es, C. Tattersall, J. Janssen, J. Manderveld, F. Brouns, H. Kurvers, and R. Koper. "Swarm-based sequencing recommendations in e-learning." In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.88.
Full textRao, Singiresu S., and Kiran K. Annamdas. "Particle Swarm Methodologies for Engineering Design Optimization." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87237.
Full textLiang, Qiao, Li Qi, and Li Chuang. "Inverted Pendulum Controller Design Based on Swarm Algorithm." In 2016 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2016. http://dx.doi.org/10.1109/is3c.2016.184.
Full textYu, Ker-Wei, and Zhi-Liang Huang. "LQ Regulator Design Based on Particle Swarm Optimization." In 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icsmc.2006.384783.
Full textMohamad Ali Tousi, Seyed, Abbas Mostafanasab, and Mohammad Teshnehlab. "Design of Self Tuning PID Controller Based on Competitional PSO." In 2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2020. http://dx.doi.org/10.1109/csiec49655.2020.9237318.
Full textDatta, Kamalika, Indranil Sengupta, and Hafizur Rahaman. "Particle Swarm Optimization Based Circuit Synthesis of Reversible Logic." In 2012 International Symposium on Electronic System Design (ISED). IEEE, 2012. http://dx.doi.org/10.1109/ised.2012.33.
Full textAlrasheed, M. R., C. W. de Silva, and M. S. Gadala. "A Modified Particle Swarm Optimization Scheme and Its Application in Electronic Heat Sink Design." In ASME 2007 InterPACK Conference collocated with the ASME/JSME 2007 Thermal Engineering Heat Transfer Summer Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/ipack2007-33256.
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